Telephone pricing structures: The effects on universal service

Joumal of Regulatory Economics; 3:293-308 ( 1991) �9 Kluwer Academic Publishers

Telephone Pricing Structures: The Effects on Universal Service

PAUL CAIN University of Pennsylvania

Department of Public Policy and Management, Philadelphia, PA 19104

JAMES M. MACDONALD Rensselaer Polytechnic Institute

Department of Economics, Troy, New York 12180-3590

Abstract We use 1987 data to study the household demand for access to the telephone system. Previous analyses find demand to be highly inelastic and, therefore, predict that local rate increases will have little impact on the goal of providing universal telephone service. We estimate that price has a considerably stronger effect on access demand, especially at low incomes, and argue that elasticities increased in the 1980s. But our evidence also suggests that the structure of telephone rates matter: where local measured service is available, changes in flat rate prices have no effect on access demand.

1. Introduct ion

Studies of telephone access demand commonly find that demand is quite inelastic with respect to price (Taylor 1980). Coupled with a finding that demand for long-distance service is considerably more elastic, the evidence supports the view that economic efficiency can be enhanced by shifting more of the financing burden for the non-traffic-sensitive (NTS) portion of the telephone system's capital plant from long-distance rates to local access prices (Kahn and Shew 1987). The required rise in local access charges would, it seems, drive relatively few people off the system, while associated declines in long-distance prices would allow substantial increases in system use and consumer welfare.

We use recent (1987) data and some alternative functional forms to reanalyze the issue. Our evidence suggests that a significant number of households (more than in earlier studies) would elect to drop their service as a result of large general increases in all local rates; in particular, low income families appear to be more sensitive to price than earlier studies indicated. But the structure of local rates also matters; increases in flat rates for monthly service, the most commonly chosen local rate option, have no effect on access demand in communities that also offer local measured service (LMS). LMS pricing plans combine a monthly access charge (lower than the fiat rate charge) with usage charges applied to local as well as long-distance calls.

294 PAUL CAIN AND JAMES M. MACDONALD

Rates on long-distance calls (about 10 percent of system usage) have for many years been set well above marginal costs, and by 1986 financed about 11 billion dollars (40 percent of the total) of NTS costs (Kahn and Shew 1987).lThe gradual entry of rival carders into lucrative long-distance services eroded the institutional basis for the financing mechanism. That erosion led in turn to a search for alternative financing mechanisms for NTS costs, such as increased local charges. Between November 1983 and April 1988, average local charges rose by 14.5% while average interstate long-distance rates fell by 39.8%, each in real terms. 2

Congress has frequently expressed concern about recent, and likely future, trends in local rates. 3 Some of the concern is over distribution: those who use lots of local service and little long-distance will pay more in the future. But the issue also concerns the realization of a telecommunications goal embodied in the 1934 Communications Act, the "universal service" goal which sought to "make available, so far as possible, to all people of the United States a rapid, efficient, nationwide and worldwide wire and radio communication service with adequate facilities at reasonable charges" (1934 Communications Act, 47 U.S.C. 214). Similar issues attend policy changes in Canada (Pike and Mosco 1986) and the United Kingdom (Hills 1989).

In practice, progress toward universal service has been measured by the percentage of households with telephone service; by that measure, the goal had been largely reached by 1980, when the aggregate penetration rate reached 93 percent, after a steady climb from 79 percent in 1960. 4 Following a decline to about 91 percent in the early 1980s, the household penetration rate rebounded back to 92.9 percent by April of 1988, in the face of the 14.5 percent real increase in average local rates during the period. Despite the high aggregate level of the household penetration rate, universal service appears distant for several groups. About 20% of households with incomes below $10,000 a year are without telephones, and that percentage rises for young and black households.

While average local charges have increased, rate structures also changed. Measured service options spread to most cities in the 1980s. States began to offer lifeline service plans, which finance reductions in local charges, to narrowly defined groups of low income and elderly residential customers. Different pricing structures likely have different impacts on the demand for access and therefore on universal service (Johnson 1988; Levin and Case 1988).

Studies of telephone access demand have been widely cited in debates over telephone pricing policy. In particular, participants often rely on Lewis Perl's (1984) point estimate of the price elasticity of demand for access in 1980 in order to analyze the effect of proposed pricing policy changes on universal service and economic efficiency. 5

Because of the changes in telephone pricing structures and regulatory policies in the 1980s, we investigate the determinants of household demand for telephone access, using 1987 data. We relate the likelihood of household telephone possession to household demographic characteristics as well as to the several prices (access, usage, and connection charges) that influence the decision to have a telephone. We use 1987 price data to investigate whether price elasticities remain quite small following the changes in pricing structure in the 1980s, and we also look more closely at the effects of differing functional forms on estimated demand elasticities.

TELEPHONE PRICING STRUCTURES 295

2. Theory, Data, and Methods

We draw our data on household characteristics from the March 1987 Current Population Survey (CPS), based on a random sample of over 58,000 households. The March survey asks whether or not a household has a telephone. CPS files also contain large amounts of demographic information, on household income, location, size, and age structure, and on householder age, marital status, ethnicity, employment status, and gender.

Our model follows the theory of telephone access demand presented in Mitchell (1978) and Park and Mitchell (1989); households will decide to have a telephone if the value of consumer surplus from using a telephone exceeds the price of access. Given usage prices (whether zero, as in flat rate service, orpositive, for measured service), the value of consumer surplus will vary directly with shifts in the demand curve for telephone usage. In Park and Mitchell (1989), the consumer surplus from usage at a zero usage price varies with the number of calls (the quantity intercept of the demand curve), the slope of usage demand, the time cost of out of house calls, and the option value of having a phone for incoming calls. In turn, they model these components as functions of income and tastes. We try to account for taste shifters more explicitly (specific variable definitions are in the Appendix).

Telephone usage should rise with household income and with household size. We further adjust these two demand shifters,by entering a quadratic term for income to account for nonlinearity and by entering family structure measures (for example, children under five) to account for calling differences among different types of family members.

People typically concentrate their telephone usage among a network of friends, neighbors, acquaintances, and co-workers. The benefits from telephone usage, and therefore the demand for access to a telephone, should increase with the size of the network. There are alternatives to using a household phone for communication with a network: the caller can use pay telephones or can walk or drive to see others.We expect that the demand for household telephone access will fall as the cost of the alternatives, including the costs of time, falls.

The dual considerations of network size and alternative communications costs drive our selection of several other demographic variables. We enter age, because we expect young householders to have lower costs of time and smaller calling networks (therefore, we expect age to be positively associated with telephone usage and access). The CPS data indicate whether the household head is of"Spanish origin" (the actual question allows the respondent to choose from the listings Cuban, Mexican American, Chicano, Mexican, Mexicano, Puerto Rican, Central or South American, and Other Spanish). We expect Spanish origin to proxy for the likelihood that Spanish is spoken at home and expect Spanish-speaking households to exhibit lower access demand, because language will restrict the size of the potential calling network and because Spanish-speaking households will often be located in Spanish-speaking neighborhoods, reducing the cost of substitutes for telephone communication. For similar reasons (smaller and more concentrated potential networks), we control for race of householder and expect whites to have a higher demand for telephone access, after controlling for age, income, and price effects.

We expect renters to have smaller calling networks than owners (because of shorter average tenure in location) and lower costs of alternatives (due to higher average population density). We therefore anticipate lower usage,and a lower access demand, for renters.


Our price data come from the April 1987 version of the Federal Communications Commission (FCC) survey of local rates in 95 cities. 6 The survey reports data for a standard flat monthly rate for local service, state and federal subscriber line charges (which vary little across cities because most states do not impose them), access and usage charges for measured service options, connection charges, and lifeline rate options. Seventy one cities from the FCC survey also appear in the March 1987 CPS file, and households in those cities form the sample of 23,137 observations.

The price data show some considerable variation across cities, as shown in table 1. For example, the mean connection charge is $46.68, with a range from the 10th percentile at $31.87 to the 90th at $60. Other access, usage, and flat rate charges also show pronounced variation across cities. Cross section price variation is important, because we would be unlikely to obtain reliable regression coefficient estimates without it. Moreover, the recent and likely future changes in local rates are well within the range of variation of the cross section data, allowing for greater confidence in prediction.

Table 1 : Monthly Local Telephone Price Data Interdecile Range

Type of Service Mean S.D. 10p - 90p Connection charges 46.68 13.14 32.15 - 67.21 Flat rate, no measured 14.89 2.93 10.81 - 16.52 service available Flat rate, measured 15.61 3.28 11.73 - 19.01 service available Measured service 7.57 1.63 5.61 - 9.77 access charge Typical measured 2.89 1.67 1.08 - 4.79 service usage charge, 50 local calls Source: Lande (1987). Note: We selected 71 cities (of 95 in the sample) that are also represented in the March 1987 Cur- rent Population Survey. Rates include the $2 federal access charge.

Some of our cities (9 of 71) do not have measured service options available. In those cities, the prices faced by a household are simple to calculate: a basic flat rate charge and a separate connection charge. Elsewhere, the typical household faces a wide variety of calling plans and associated price schedules. We enter two, in addition to the connection charge. We use the basic flat rate service charge because that is a popular choice (Lande and Wynns 1987). We also enter the price of lowest cost measured service option for minimal calling (a"budget" option), and we break this option into access and usage charges. We expect that this measured service option should be the relevant choice for those households on the margin of a telephone service decision.

Mitchell (1978) and Park and Mitchell (1989) argue, on theoretical and empirical grounds, that the price elasticity of demand for access should vary with income. In our empirical analysis, we use a logit specification for the dichotomous dependent variable. Predicted values in the logit model are logarithms of the odds ratio P/(1-P), where P is the probability of having a telephone. The logit specification imposes a particular interaction


between income and the price elasticity of demand: the effect of a change in the access price on P varies with the starting value of P, which in turn depends on income.But because the linear logit specification imposes one particular form of income-price interaction, we also experiment with more flexible price-income interaction effects in the empirical analysis. We discuss the development of our more flexible functional form in more detail later in this report.

3. Regress ion Resul ts

Table 2 reports coefficients on the price terms of logit regression analyses of household telephone possession (full results are reported in Appendix B). Columns (1) and (2) report results from a linear specification of the price terms, while column (3) reports on results from a piecewise interactive specification that allows price effects to vary with income.Columns (1) and (3) use a sample of all 71 cities, while column (2) uses the 62 cities offering local measured service.

Predicted values in the logit model are logarithms of the odds ratio P / ( 1 - P); because of the Izansformation, one cannot easily interpret the coefficients from the logit regression analyses presented in table 2. To aid interpretation, table 3 shows the effects on P, the

!Table 2: Price Coefficients from Logit Analysis of Whether a Household has a Telephone Flexible Price

Measured Specification, Explanatory Variables All Cities Service Cities All Cities

(1) (2) (3) Connection Charge -.0143 -.0165 -.0142

(.0059) (.0061) (.0059) Flat Rate, No M.S. Option -.1027 -. 1400 Available (. 0382) (. 0400) Flat Rate, No M.S., times Income .0023 Below 60th Income Percentile (.0010) Flat Rate, No M.S., Above 60th .0864 Income Percentile (.0320) Flat Rate, M.S. Option Available .0072 .0066 .0061

(.0119) (.0120) (.0120) M.S. Access Charge -.0952 -.0955 -.1825

(.0215) (.0216) (.0258) Access Charge, Times Income, - - .0069

Below 60th Income Percentile (.0011 ) Access Charge, Above 60th m m .2280 Income Percentile (.0430) M.S. Usage -.2241 -. 1435 -. 1378 Charge (1.0270) (1.3698) (1.3864) M.S. -1.0104 - - -1.1602 Dummy (.5513) (.6309) Full results reported in Appendix B. "MS." = measured service.


probability of owning a phone, of several changes in relevant independent variables, using the full sample regression coefficients (column 1) from table 2. We assume in table 3 that other variables are such as to yield each of 4 different initial probabilities of telephone ownership (95, 90, 80, and 70 percent probabilities). We then ask how each of the indicated changes will affect P, for each different starting value. Since the national average telephone penetration rate is about 93 percent, the higher two percentages bracket the national average, while the lower two are relevant for several specific groups (such as low income households, racial minorities, or young urban males). Table 4 reports elasticity calculations for the price changes reported in table 3, while table 5 shows the sensitivity of elasticity estimates to differing functional forms.

3.1. Demographic Variables The demographic effects, especially those relating to income, age, education, gender, and

tenure, are quite similar to those reported in other studies of United States (Perl) and Canadian (Bell Canada) households. As in those studies, demographic characteristics have strong effects on penetration rates (Appendix B).

The coefficient on income is positive and highly significant statistically. Education has a statistically significant and positive coefficient. For an initial probability of 90%, reducing the highest grade completed from 12 to 10 drops P to 87.4%, other things equal. Householder age has a positive effect, large at low initial probabilities, as can be seen in the table 3 example.

Several other less common demographic variables have strong impacts. In particular, the coefficient on Spanish ethnicity is negative and relatively large (table 3). The results also indicate that nonwhite households in central city neighborhoods are considerably less likely to own a telephone (table 3). The coefficient on rent (one if the householder rents, rather than owns the home) is negative, statistically significant, and quite large.

Table 3. Effect of Changes in Selected Variables on Telephone Access Probabilities Original Probability

Event Changes 95

To Spanish ethnicity 92.1 Suburban white to central city, nonwhite 90.2 35 years old to 70 97.7 $20,000 income to $10,000 92.0 $45 connection charge to $55 94.3 $15 flat rate to $17.50 93.6 $15 flat rate to $25 87.3 $7.50 measured service access charge 93.7 to $10.00 $7.50 measured service access charge 88.0 to $17.50

90 80 70 New Probabilities

84.6 71.1 58.9 81.3 66.0 53.1 95.3 90.0 84.0 84.5 70.9 58.6 88.7 77.6 67.0 87.4 75.6 64.4 76.3 58.8 45.5 87.6 75.9 64.8

77.6 60.7 47.4

Note: Estimates based on coefficients from column (1), table 2. Flat rate effects are for cities without measured service.


3.2. Price Variables Coefficients on the key price variables are as expected: negative, statistically significant,

and small enough to indicate that demand is quite inelastic. Table 4 shows estimated arc price elasticities for the price changes of table 3. Connection charges have a statistically significant, negative but relatively small coefficient, implying rather modest changes in penetration rates for a $10 change in connection charges (table 3). Elasticities of demand range from .037 to .22, in absolute values, with greater elasticities in households with lower initial probabilities.

Only 9 of our 71 sample cities had no measured service options available by April, 1987. In those cities (9 separate price observations and 2,118 households), fiat rate charges had a negative coefficient that was small but statistically significant at a .01 level of significance, with implied absolute value arc price elasticities (for the $2.50 increase in table 3) that range from. 10 at the initial ownership probability of .95 to .54 at a .70 initial probability (table 4).

Measured service access charges, in the 62 cities that offer the option,should be the relevant price for those households on the margin for having a telephone. Relatively few subscribers choose this sort of plan, but we expect it to be the relevant price for marginal buyers. The coefficient on access charges is negative and statistically significant at the .01 level.

For the $2.50 access price increase in table 3, the implied absolute value arc price elasticities range from .045 to .255. Note that these are less than one half the size of the flat rate elasticities. The coefficient values are nearly identical, but mean flat rates are substantially higher than mean measured service rates. As a result, calculated percentage price changes are larger for measured service (tables 1 and 3), and elasticities are correspondingly smaller. The usage charge for measured service has the expected coefficient sign (negative) but is not statistically significant.

Notice in particular that price changes in flat rate options have no significant effect on penetration rates where measured service is available (table 2): the coefficient on flat rate charges was positive, small, and not statistically significant. The result suggests that telephone penetration rates are responsive to the structure of telephone rates, and in particular to measured service budget access charges, but may be quite insensitive to changes in the overall level of rates, which are dominated by flat rate charges, as long as measured service rates remain unchanged.

Our original specification imposed a linear relation between the explanatory variables and the dependent variable. Because the dependent variable is the log of the odds ratio, the logit specification imposed some interaction effects on the model. For example, the impact

Table 4. Estimated Price Elasticities of Demand

Initial Price and Change $45 Connection Charge, to $55 $15 Flat Rate, to $17.50 $7.50 Measured Service Access Charge, to $10

lnitialTelephone Probability .95 .90 .80 .70 .038 .076 .152 .226 .096 .187 .376 .546 .048 .095 .185 .271

Note: Absolute value arc estimates, calculated for above price changes, from equation (1), table 2. Flat Rate elasticities are for cities without measured service.


of a given price change on P varies with the initial value of P, and therefore estimated price effects and elasticities will vary with levels of income, age, initial prices, and other explanatory variables. This sort of interactive specification is consistent with the theory of telephone demand (Mitchell 1978; Park and Mitchell 1989) but the particular interactive relation is imposed by the logit specification. In column (3) of table 2, we alter the specification, in order to allow for a more flexible interrelation between prices, income, and access demand. Since the results follow on some experimentation, let us first describe the process of our specification search.

We initially stratified our sample into income quartiles, and estimated four separate logit regressions, using the specification of column 1, table 2. The demographic variables showed little systematic change in coefficients, signs, or significance levels, but the price variables showed large changes. For the two upper quartiles, the price coefficients became small and not significantly different from zero, while price effects were significant, negative, and considerably larger among low income groups.

In our next step, we used the original large sample, but allowed the price terms to take on different values for each income decile. 7 With that specification, we found that the four top deciles each had price coefficients that were close to zero and not significant, that the lowest decile had a much larger price effect than the original specification suggested, and that price terms moved rather smoothly towards zero as we moved away from the lowest decile.

Table 5: Elasticity Estimates When Price Effects are Allowed to Vary With Income. Household Income

Initial P Model 3,000 5,000 10,000 15,000 25,000 .70 c .271 .271 .271 .271 .271 .70 v .488 .442 .331 .225 .030 .80 c .185 .185 .185 .185 .185 .80 v .335 .316 .226 .152 .020 .90 c .095 .095 .095 .095 .095 .90 v .172 .155 .115 .079 .012 .95 c .048 .048 .048 .048 .048

.95 v .086 .078 .056 .037 .005 Note: Absolute value arc price elasticities calculated for 2.50 access price increases from 7.50 original price, for models in which price effects are constant (c) and in which price effects are allowed to vary with income (v).

Based on the information obtained from using income deciles, we imposed the following piecewise interactive specification for access charges and for flat rates in cities with no measured service:

(b 1 * Price) + (b 2 * Price * Income * D1) + (b 3 * Price * D2)

where bl, b2, and b3 are the relevant coefficients, Price is the particular price term (either access charges or flat rates), Income is household income, D1 is a dummy variable equal to one if household income is less than the 60th decile, and D2 is a dummy variable equal


to one for household incomes at or above the 60th decile. With this specification, price effects are allowed to vary with income up to the 60th decile and are constant across upper income levels. We expect bl to be negative and relatively large, b2 to be positive, and b3 to be positive and approximately equal to bl in absolute value. Results on the price terms are reported in column (3) of table 2, where the price coefficients in the piecewise interactive specification have the expected signs and are each highly significant (again, full regression results are reported in Appendix B). 8 To facilitate comparisons, table 5 reports elasticity estimates for the linear and piecewise interactive models, for several income levels. The results indicate that low income households (less than $10,000 annual household income) are considerably more sensitive to price changes in the piecewise interactive specification. Estimated elasticities almost double for the lowest income strata ($3000) and are about one-fifth higher at the $10,000 level of income. But middle income and wealthier households are quite insensitive to price changes: estimated elasticities at the $25,000 income level fall by about 90 percent compared to the linear specification. Lower income households account for almost all of the price sensitivity in the data.

Flat rate prices continue to have essentially no effect on access demand in cities with local measured service. Moreover, flat rate prices had no significant effect, where local measured service was offered, when we experimented with various income interactions.

4. Impl ica t ions a n d C o m p a r i s o n s to O t h e r Studies

Access demand has been uniformly found to be quite inelastic. Lewis Perl's two earlier studies (1978; 1984) are widely cited and widely acknowledged to be the most authoritative. (See the references in footnote 5.)

Perl used household data from the 1980 Census of Population, which identifies broad geographic locations for specific households (no location of less than 100,000 households is identified); Perl matched broad household locations to internal AT&T rate data, while our city-specific price data are drawn from the FCC survey. 9 In practice, the price data are at a comparable level of aggregation. Our sample is confined to households in metropolitan areas, while Perl's includes some in nonmetropolitan areas.

In Perl's 1980 data, the average household penetration rate was .932, and he estimated an average price elasticity at that point of-.038.1~ But point estimates can be misleading; for many purposes we ought to look at the effects of large percentage changes in local prices. With his results, Perl shows that a $10 increase in the flat rate charge (from $10 to $20) would reduce predictedp to .893, while a $10 increase in the measured service access charge would reduce predictedp to .901. Our coefficient values are substantially higher than Perl's: using our model and a starting value of.932, a $10 increase in flat rates will reduce predicted P to .810, while a $10 increase in measured service access charges will reduce predicted P to .841. (See table 3 for calculations at different starting values.) Since 90.8% of our households are in areas that offer measured service, the average sample decline would be 9.2 percentage points, to .840. Perl's study predicts an average decline, after a $10 increase in each price, of 3.6 percentage points, to .896.

These calculations are in nominal dollars. Ten 1980 dollars were equivalent in real terms (using the CPI) to $13.60 in 1987. Using real dollars (a $13.60 1987 increase) leads to a


predicted average sample decline of 15.1 percentage points, to .782, substantially higher than that predicted using 1980 data. Our more flexible specification (column 3, table 2) indicates that this is an average response; low income households would have much stronger reactions, and high income households much weaker.

The differing results are unlikely to simply reflect the modest sample differences. Our sample consists of CPS households in 71 metropolitan areas, while Perl's one percent sample from the 1980 Census of Population includes nonmetropolitan as well as metropolitan households. In 1987, 77 percent of the population lived in metropolitan areas. Given the difference in estimated reactions to price changes in the two studies, the relatively small nonmetro population would need to have an extraordinarily inelastic access demand, for sample differences to account for the results. But Perl's results, as well as those of Bell Canada (1988), appear to show that rural users are less likely, all else equal, to have telephones. Given the structure of the logit model, rural users would consequently have more elastic access demands. If our sample included rural users, estimated average price elasticities would therefore most likely be higher, not lower.

Suppose the results in each sampl9 are accurate; then the results indicate that access demand in 1987 may have become more sensitive to price than it was in 1980..If true, the development is unexpected. Other writers predicted a steady decline over time in access price elasticities (Wenders 1987; Perl 1984; Gordon and Haring 1984); if demand is more price sensitive among lower income households, then real income growth should lead to steady declines in price elasticities over time. Gordon and Hating (1984) argued that increasing local prices during the 1980s should have led to no decline in telephone penetration rates, because the price elasticity of demand was small, and rising real incomes would offset any price effect. Indeed, telephone penetration in the United States stood at 93 percent of households in March of 1987,just about where it had been in Perl's 1980 data, despite rising real local rates in the 1980s.

We offer another interpretation of recent developments, one that focuses on households at the margin for telephone demand. Rising real incomes among affluent households have little effect on telephone penetration rates or price elasticities, since those households are extremely unlikely to be without telephones. Table 5 shows that it is important to focus on households at the margin, who show relatively high price elasticities in our model. Poor households showed modest real income growth between 1980 and 1987, according to CPS data. In 1980, 19.3% of households had incomes below 10,000 1987 dollars; 18.4% fell below that level in 1987 (and the proportion with incomes under 3,000 dollars grew). But the characteristics of low income households changed markedly, in ways that should lead to declines in telephone penetration and more elastic access demands. Note, in table 3 and Appendix B, that householder age, ethnicity, location, and tenure status (rent or own home) have large estimated effects. Between 1980 and 1987, the proportion of Hispanic householders, among all poor families, rose from 12.1% to 16.8%. The proportion of low income families who own their home fell from 43.4% to 34%. The median age of low income householders fell by a year, from 38.2 to 37.2. The proportion of poor households who were nonwhite and living in central cities rose from 17.7% to 19.6%. Assuming an initial predicted telephone penetration rate of .8 among low income households, the above shifts would lower predicted penetration rates to .766. According to the evidence of table 5, we ought as a result to see small increases in absolute value price elasticities. With higher


elasticities among groups on the margin, and essentially zero price elasticities among middle and upper income households, we should expect to see some increase in estimated average price elasticities. We caution that this evidence is merely suggestive; stronger conclusions would require the use of household and price data pooled across the relevant years.

Plausible evidence suggests that proportionately more households may have moved closer to the margin, for telephone access demand, in the 1980s. Why didn't penetration rates fall? We suspect that, contrary to the message implied in the CPI, real minimum access prices actually fell for many households in the 1980s. In Perl's 1980 data, 54% of households were in areas that offered local measured service. Measured service offers a relatively low monthly fixed charge ($5.70, on average, in Perl's data, compared to $9.05 for flat rate service), with per call usage charges. Our data show a mean access price, in 1980 dollars, of 5.79 in 1987, 1.6% above Perl's, while our mean real flat rate price was 27.7% higher. (Telephone sets were detariffed in the 1980s, so we should impute a telephone rental to the 1987 data in order to maintain comparability with 1980; if we follow Perl and Taylor's approach, imputation will add an additional $2 to each 1987 price.) But measured service options spread in the 1980's, so that 90.8% of our households could use them. In brief, measured service was available to about one half of urban households in 1980 and 1987, and those households saw a modest rise in the minimum real price of access; over a third of urban households saw substantial declines in minimum real access prices as measured service became newly available, and about one tenth saw substantial real increases in flat rates, with no measured service available. Our results support the evidence offered in Park and Mitchell's (1989) simulation of the effects of measured service on access demand, where introduction of measured service options, as flat rates rose,led to increases in subscribership, especially among low income households.

Perl's (1984) study shows that the structure of local telephone rates can have an important impact on telephone demand. In his analysis, the estimated demand elasticity for flat rate charges fell by about two thirds when measured service options were present. That's an important finding, because it indicates that more of the NTS financing burden could therefore be shifted to local charges from long-distance, without discouraging universal service, by raising flat rates, expanding local measured service, and offering low priced access through budget and lifeline measured service options.

If anything, our evidence offers stronger support for the role of the local rate structure. Where measured service is not available, our reported demand effects from flat rate price changes are larger than Perl's (table 3). But when measured service options are available, price changes for flat rate service have essentially no effect on access demand in our analysis.

These estimates suggest that universal service can be maintained and expanded, even while more of the NTS financial burden is shifted to local charges. In particular, since telephone subscribership is sensitive to measured service access charges, universal service goals can be met, at relatively low cost, by introducing and expanding budget measured service options. Our calculated elasticity estimates also imply that appropriately targeted lifeline telephone assistance programs can be effective mechanisms for inducing expansion of telephone subscribership.rl

Financing goals can be reached through increases in local flat rate prices, while main- taining budget measured service access charges. Flat rate price changes have no effect on access demand in our model, when measured service is available. Most households opt for


flat rate options, according to NARUC (1987) data, but it is likely that increases in flat rate prices would cause some shift of subscribers toward budget measured service (Train, McFadden, and Ben-Akiva 1987). But that shift, and its attendant revenue losses, can be mitigated by imposing relatively high usage charges on budget measured service. Our data show no effect of usage charges on access demand. As in the traditional theory of telecommunications pricing (Brown and Sibley 1986; Mitchell 1978), our results indicate that there is no necessary conflict between financing and universal service goals, if regulators choose the appropriate structure of local rates.

Appendix A: Variable Definitions and Data Sources

Demographic Variables. Source: CPS March 1987 File. Income--- Household income. Householder Education--Highest grade completed for the householder. Householder Age---Self explanatory. Householder Size--Number of persons in the household. Percent Age 1-5--Percent of people in household aged 1 to 5. Percent Age 6-1 lmPercent of people in household aged 6 to 11 Percent Age 12-18--Percent of people in household aged 12 to 18. Recent Migrant--Dummy variable equal to one if the householder lived elsewhere 1 year

ago. Rent Home--Dummy variable equal to one if the home is rented. Male Householder--Non-family household, male head. Single Householder--Dummy variable, equal to one if householder is unmarried. Central City Householder--Dummy variable equal to one if household is in the central city

of an SMSA. Nonwhite Householder--Self explanatory. Spanish Spoken at Home--Dummy variable, equal to one if householder is of Spanish origin.

Price Data. Source: Local Rates Update (FCC), April 1987. Connection Charge--Total charge, including taxes, for initiating service to a residence. Flat Rate, No M. S. Option~Equal to zero where measured service is available. Where

measured service is not available, equal to monthly charge for private line, rotary phone, unlimited calling service.

Flat Rate, M. S. Available--Equal to zero where measured service is not available. Where measured service is available, the monthly charge for private line, rotary dial, unlimited call service.

Access Charge, M. S.--Monthly flat rate charge for access under least cost measured service plan. Set equal to zero where measured service is not available.

M. S. Dummy--Dichotomous variable, equal to one in areas that offer measured service, and zero elsewhere.

Usage Charge, M. S.--Charge for 50 typical local calls under least cost plan of all available measured service plans (for 50 calls).

D1--Dummy variable equal to one for household incomes below the 60th percentile of the distribution of household income.

D2--Dum my variable equal to one for households with income at or above the 60th percentile.


Appendix B. Logit Analysis of Whether a Household Has a Telephone Coefficients and Standard Errors

Measured Service

Independent Variables All Cities Cities All Cities All Cities (1) (2) (3) (4)

All C i t i e s (5)

Intercept 3.0902 2.2140 1.3476 4.5799 3.7137 (.7177) (.4460) (.5548) (.5440) (.7221)

Connection Charge -.0143 -.0165 -.0136 -.0177 -.0143 (.0059) (.0061) (.0023) (.0022) (.0059)

Connection* Migrant -.0065 -.0045 m - - -.0068 (.O050) (.0051) (.0O50)

Connection* Renter .0083 .0105 m m .0085 (.0064) (.0067) (.0064)

Flat Rate, No M.S. -.1027 - - -.0631 -.0594 -.1400 Available (.0382) (.0198) (.0343) (.0400) Flat Rate, No M . S . , - ~ ~ w .0023 * Income * D1 (.0010) Flat Rate, No M.S., - - ~ - - .0864 * D2 (.0320) Flat Rate, M.S. .0072 .0066 .0250 .0273 .0061 Available (.0119) (.0120) (.0111 ) (.0107) (.0120) M.S. Access Charge -.0952 -.0955 -.0631 -.0668 -.1825

(.0215) (.0216) (.0198) (.0185) (.0258) M.S. Access Charge . . . . .0069 * Income * D1 (.0011) M.S. Access Charge . . . . .2280 * D2 (.0432) M.S. Usage Charge -.2241 -.1435 -.7101 -1.5712 -.1378

(1.0270) (1.3698) (1.2567) (1.1446) (1.3864) M.S. Dummy -1.0104 - - -.7450 -.7290 -1.1602

(.5513) (.5036) (.4954) (.6309) Income .0555 .0556 .0885 ~ .0128

(.0035) (.0037) (.0030) (.0085) Income Squared -.00019 -.00019 -.0027 - - -.00006

(.000O2) (.00002) (.0002) (.00004) Householder .1300 .1293 w m .1282 Education (.0101) (.0105) (.0102) Householder Age .0231 .0231 .0344 - - .0239

(.0024) (.0025) (.0017) (.0024) Household Size -.0728 -.0751 ~ ~ -.0769

(.0305) (.0317) (.0306) Percent Age 1-5 -.9309 -1.0136 ~ - - -.8660

(.2120) (.2201) (.2133) Percent Age 6-11 -.1073 -.0607 - - - - -.0573

(.2374) (.2476) (.2387) Percent Age 12-18 .2055 .3525 ~ - - .2228

(.2741) (.2992) (.2749)


Appendix B. Continued Recent Migrant .0045 -.1124 - - - - .0334

(.2507) (.2602) (.2521) Rent Home -1.4467 -1.5686 - - - - -1.4702

(.3292) (.3466) (.3285) Male Householder -1.2968 -1.2914 - - - - -1.2676

(.2217) (.2376) (.2215) Single Householder -.1194 -.1066 ~ - - -.0772

(.0884) (.0923) (.0893) Central City Location -.1183 -.1518 ~ - - -.1039

(.0746) (.0796) (.0763) Nonwhite -.1806 -.3037 ~ ~ -.1559 Householder (.1545) (.1650) (.1556) Spanish Origin -.4889 -.5123 ~ - - -.4977

(.0862) (.0910) (.0864) Central City* -.4261 -.3154 ~ - - -.4359 Nonwhite (. 1722) (. 1830) (. 1733) Single* Male .3810 .3922 - - - - .3245 Householder (.2334) (.2494) (.2335) Households 23,137 21,019 23,137 23,137 23,137 Cities 71 62 71 71 71

Notes

We thank two anonymous referees and seminar participants at RPI and at the Rutgers Public Utilities Conference for comments on earlier drafts.

1. Kahn and Shew (1987) cite testimony by Lewis Perl that, in Kentucky, intrastate rates were more than 4 times and interstate rates more than 3 times incremental costs. They cite estimates by Southwestern Bell of a ratio of interstate toll rates to incremental costs of six to one in Oklahoma, and cite New England Telephone estimates of an eleven to one ratio. Such high rates lead to welfare losses by discouraging use and by attracting high cost entrants.

2. According to consumer price indexes (CPI) for "telephone, local charges" and "telephone, interstate toll calls" and using the overall CPI to adjust to real terms.

3. See, for example, Transition in the Long Distance Telephone Industry, Hearings before Committee on Energy and Commerce, U.S. House of Representatives, February 19 and 20, 1986;Local Telephone Rate Increases, Staff Report for the Committee on Energy and Commerce, U.S. House of Representatives, February 1984; The Economic Issues of a Changing Telecommunications Industry, Hearings before the Joint Economic Committee of the Congress, October, 1983; or The Impact of the FCC Telephone Access Charge Decision, Hearings before the Committee on Government Operations, U.S. House of Representatives, September 1983. Vietor and Davidson (1983) describe the strong political reaction against the FCC's access charge plan, which initially proposed large increases in local rates to offset large declines in long-distance rates.

4. Statistical Abstract of the United States, 1988,p. 523. 5. See, in addition to the testimony cited in footnote 3, Kahn and Shew (1987), Wenders (1987), Gordon and

Haring (1984), Congressional Budget Office (1984), Pike and Mosco(1986), Fuhr (1986), Wenders and Egan (1986), Griffin and Mayor (1989), Johnson (1988), Park and Mitchell (1989), and Kaserman, Mayo, and Flynn (1990).

6. Lande (1987). 7. That is, we altered the specification of equation 1, table 2, replacing the measured service access charge

term with


10

~ (Bi * DI * A), i=1

whereA is the access charge,Bi are separate coefficients, andDi are dummy variables, equal to one if the household's income falls into the ith income decile, and zero otherwise.

8. We compared these several specifications using chi square tests on likelihood ratios. If we denote Model I as a linear specification for fiat rates and access charges, Model rl as a linear specification on fiat rates and piecewise interactive on access charges, Model HI as a linear specification for flat rates and decile-specific access price coefficients for all income declles below 60, Model IV as a piecewise interactive specification for access charges and fiat rates, and Model V as continuous income-price interaction terms for access charges and fiat rates, then the results were as follows:

Test Statistic Critical Value (.05)

Model I1 vs I 24.02 5.99

Model II vs III 7.58 9.49

Model IV vs II 14.14 5.99

Model IV vs V 36.80 9.49 The results show that Model IV (column 3, table 2) gives a statistically significant improvement in fit, compared

to the specification imposed in column 1 of table 2 (Model I) and compared to the use of simple price income interactions (Model V). Further flexibility (Model m ) does not provide statistically significant improvements in fit.

9. The Census of Population asks the "telephone question" in a different wording than the CPS. Wording differences may account for some difference in estimated penetration rates, but there's no strong reason to expect them to account for the differing price effects. Perl and Taylor (1990) investigate the wording issue and do not find conclusive differences in effects.

10. At a mean penetration rate of .93, we estimate the access demand elasticity to be -.066, almost identical to Kaserman, Mayo, and Flynn's estimate, using 1986 statewide averages, of -.068, close to Perl's estimate of -.038, and certainly consistent with the common view that access demand is extremely inelastic. But one should exercise caution here; our estimate is a weighted average of a fiat rate estimate of -. 134 and a measured service estimate of -.061, with weights of .092 and .908 respectively. The aggregate estimates are closer than the components because the weight assigned to measured service grew from 54% in 1980 to 90.8% in 1987. Furthermore, because measured service prices start from a low base, the usual envisioned access price changes (2 to 10 dollars) represent large percentage changes. Incidentally, although later studies commonly cite Perl's point elasticity estimate and often interpret it as a constant over the relevant range of the demand curve, Perl generally calculated incremental penetration changes for each price change in his paper.

11. Lifeline programs provide subsidies to all eligible telephone subscribers, only a fraction of whom subscribe to telephone service because of the subsidy. Because our results indicate that access demand among low income households is considerably more sensitive to price than previously believed, we expect that the fraction will be somewhat larger, and that lifeline programs can be effective means of furthering access to the telephone system. Lifeline programs designed to expand universal service would be limited to support of measured service access charges and would target households with relatively elastie demands: (low income and relatively young---many programs exclude householders under 60) and would include outreach to current nonsubscribers (Johnson 1988).

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Telephone pricing structures: The effects on universal service

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